Area recognizing device for image signals

ABSTRACT

A first converting circuit converts I-level image data, and suppresses a diffusion range of data by an N-level converting circuit. The N-level converting circuit converts a K-level image into an N-level gradation image (K&gt;N). An image memory stores the N-level gradation image. An estimating circuit converts the N-level gradation image into an M-level gradation image (N&lt;M). A second converting circuit returns the M-level gradation image into the form before the conversion according to a conversion amount from the first converting circuit. Accordingly, density ripple can be suppressed even in a small scanning aperture. A control circuit sets a conversion table to the first converting circuit and the second converting circuit according to a setting signal from a control panel, and outputs a control signal to the estimating, circuit, so that processing mode is set in response to different images, and image quality is adjusted.

This application is a division of Ser. No. 08/700,822, filed Aug. 12,1996, now U.S. Pat. No. 5,930,007, and a division of Ser. No.08/321,178, filed Oct. 11, 1994, now U.S. Pat. No. 5,760,922.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an area recognizing device forrecognizing an edge area and a non-edge area of an image which isexpressed by an N-level gradation level, and to a gradation levelconverting device employing the area recognizing device (N<M).

2. Description of the Related Art

Recently, outputting devices, such as printing devices and displaydevices, print and display multi-level data. When image information isprocessed electronically, the amount of information is inevitablyincreased.

In order to tackle the problem of the increased amount of information, afacsimile apparatus, a still image file, and a copy device convert anoriginal image into binary signals, then transmit, store, and processthe binary signals. However, image quality is degraded if the binarysignals are directly reproduced with the outputting devices.

To improve image quality, the binary signals are converted intomulti-level gradation signals by the outputting devices. Consequently,the degradation of image quality is avoided.

A dither method and an error-diffusion method are employed to expressthe intensity of color by binary signals, and the binary signals of thisdigital halftone image are converted into multi-level signals by awell-known halftone image estimating method. For example, "METHOD ANDAPPARATUS FOR ESTIMATING HALFTONE IMAGE FROM BINARY IMAGE" cited in U.S.Pat. No. 4,758,897 is a well known method. With this method, a halftoneimage is estimated based on a ratio of black and white pixels within ascanning aperture to be scanned. More specifically, a larger scanningaperture is selected in an area where the fluctuation in pixel level issmall, while a smaller scanning aperture is selected in an area wherethe fluctuation in pixel level is large. Then, a halftone image isestimated based on a ratio of black and white pixels within the thusselected scanning aperture.

Also, the binary signals are converted into the multi-level signals byeliminating any influence of patterns of the original image beingdifferent depending on cycles by detecting edges with a plurality ofdifferentiators, and selecting the detected edge signal (for example,Japanese Laid-Open Patent Application No. 3-250975).

Also, edges of the binary-level image are detected by a singleedge-detection filter, then an edge area, a non-edge area, and anintermediate area are recognized based on outputs from theedge-detection filter. The degree of smoothness of the image is adjustedby changing weighing coefficients to be added within the scanningaperture (for example, Japanese Laid-Open Patent ApplicationNo.4-51378).

However, desired uniformity and resolution are not obtainedsimultaneously by simply controlling the aperture size and weighingcoefficients of a filter which estimates a halftone image based on aratio of black and white pixels within the scanning aperture of thebinary image.

Also, to implement the method of detecting edges of the binary imagewith a plurality of edge filters, and selecting a single edge filterbased on the detected result, the process of switching between an edgearea and a non-edge is needed. Consequently, negative effects ofdetection errors appear in a display image. With a single edge detector,on the other hand, the detection is influenced by the texture of a dotarrangement pattern in converting into the binary signals; as a result,recognition errors frequently occur.

SUMMARY OF THE INVENTION

It is a first object of the present invention to provide a gradationlevel converting device for converting a gradation level optimally bothin an edge and a non-edge area of an image, and simultaneouslymaintaining desired uniformity and resolution, so that desired imagequality is obtained.

It is a second object of the present invention to provide an arearecognizing device for recognizing an edge area and a non-edge area ofan image before gradation level conversion so that the gradation levelconversion can be performed rationally.

It is a third object of the present invention to provide a device whichis suitable for a system which converts a high-level gradation imageinto a low-level gradation image for the purpose of storing the image,then returns the low-level gradation image into the high-level gradationimage to be outputted, the device further being capable of reproducingthe high-level gradation image with high reproductivity.

The first object may be fulfilled by a gradation level converting devicefor converting an image which is expressed by an N gradation level intoan image which is expressed by an M gradation level (N<M), the gradationlevel converting device comprising a first smoothing filter whichcomprises a scanning aperture which includes a pixel in concern andpixels which are located around the pixel in concern of the N-levelgradation image and converts the N-level gradation image into theM-level gradation image by multiplying each of the pixels within thescanning aperture by a certain weighing coefficient and adding themultiplied pixels, a second smoothing filter possessing the degree ofsmoothness which is greater than the first smoothing filter since anaperture size and a weighing coefficient are different from the firstsmoothing filter, and converting each of the pixels from the N gradationlevel into the M gradation level according to the degree of smoothness,an edge detecting unit for detecting an edge component of the pixel inconcern according to each of the first smoothing filter and the secondsmoothing filter, a mixing ratio calculating unit for calculating amixing ratio from the detected result of the edge detecting unit, amixing unit for mixing the converted result outputted from the firstsmoothing filter and the converted result outputted from the secondsmoothing filter based on the calculated mixing ratio, an arearecognizing unit for recognizing the edge area and the non-edge area ofthe N-level gradation image, and a selecting unit for selecting themixed result outputted from the mixing unit for the non-edge area of theN-level gradation image and selecting the converted result from thefirst smoothing filter for the edge area as referring to the recognizedresult from the area recognizing unit.

The area recognizing unit may comprise a plurality of filters each ofwhich detects an edge component of a pixel in concern as referring to avalue of a pixel within an area including the pixel in concern, thefilters possessing an identical edge detection characteristic and thepixel concern in each of the filters being set at a different locationin the N-level gradation image, an adding unit for comparing the outputfrom each of the plurality of filters with a certain value, and addingthe output only when it is greater than the certain value, a judgingunit for comparing the added result from the adding unit with a certainjudgement level, and outputting the compared result as a judgement, anda recognizing unit for recognizing a part of the image at which each ofthe filters is located as the edge area when the judgement leveloutputted from the judging unit is a first judgement, and recognizing itas the non-edge area when the judgement is a second judgement level.

The gradation level converting device may further comprise a settingunit for setting a plurality of processing modes, or an image qualityadjustment value, and a first controlling unit for controlling thecertain judgement level according to a setting by the setting unit.

The area recognizing unit may comprise a plurality of first filters eachof which detects an edge component of a pixel in concern as referring toa value of a pixel within an area including the pixel in concern, thefilters possessing an identical edge detection characteristic and thepixel in concern in each of the filters being set at a differentlocation in the N-level gradation image, a plurality of second filterswhich possess an identical edge detection characteristic which isdifferent from the plurality of first filters, and the pixel in concernin each of the filters is set at a different location in the N-levelgradation image, a first adding unit for adding the detected resultoutputted from each of the plurality of first filters, a second addingunit for adding output from each of the plurality of second filters, afirst judging unit for comparing the added result outputted from thefirst adding unit with a first judgement level, and outputting thecompared result as a judgement, a second judging unit for comparing theadded result from the second adding unit with a second judgement level,and outputting the compared result as a judgement, and an arearecognizing unit for recognizing whether a part of the image where thefilter is located is the edge area or the non-edge area according to thejudgements from the first judging unit and the second judging unit.

The gradation level converting device may further comprise a settingunit for setting a plurality of processing units, or an image qualityadjustment value, and a second control unit for controlling validity ofthe judgement from one of the first judging unit and the second judgingunit according to a setting by the setting unit.

The first object may also be fulfilled by a gradation level convertingdevice for converting an image which is expressed by an N gradationlevel into an image which is expressed by an M gradation level (N<M),the gradation level converting device comprising a first smoothingfilter which comprises a scanning aperture which includes a pixel inconcern and pixels which are located around the pixel in concern of theN-level gradation image and converts the N-level gradation image intothe M-level gradation image by multiplying each of the pixels within thescanning aperture by a certain weighing coefficient and adding themultiplied pixels, a second smoothing filter possessing the degree ofsmoothness which is greater than the first smoothing filter since anaperture size and a weighing coefficient are different from the firstsmoothing filter, and converting each of the pixels from the N gradationlevel into the M gradation level according to the degree of smoothness,an edge detecting unit for detecting an edge component of the pixel inconcern according to each of the first smoothing filter and the secondsmoothing filter, a mixing ratio calculating unit for calculating amixing ratio from the detected result of the edge detecting unit, amixing unit for mixing the converted result outputted from the firstsmoothing filter and the converted result outputted from the secondsmoothing filter based on the calculated mixing ratio, an enhancing unitfor enhancing the M-level gradation image outputted from the firstsmoothing filter, and outputting the enhanced image, an area recognizingunit for recognizing the edge area and the non-edge area of the N-levelgradation image, and a selecting unit for selecting the mixed resultoutputted from the mixing unit for the non-edge area of the N-levelgradation image and selecting the converted result from the enhancingunit for the edge area as referring to the recognized result from thearea recognizing unit.

The first object may be fulfilled by a device for converting input imagedata which is expressed by an N gradation level into image data which isexpressed by an M gradation level (N<M), the device comprising aplurality of edge detecting units with different characteristics eachfor detecting an edge amount of the N-level gradation image data, anextracting unit for mixing the detected results outputted from each ofthe plurality of edge detecting unit to generate an edge extractionamount, a plurality of spatial filters for smoothing the N-levelgradation image with different weighing coefficients or smoothingdifferent smoothing areas of the N-level gradation image data, a mixingunit for normalizing the smoothed result outputted from each of theplurality of spatial filters, and mixing the normalized result with acertain mixing ratio according to the edge extraction amount outputtedfrom the extracting unit, and an outputting unit for defining the mixedresult outputted from the mixing unit in the M gradation level, andoutputting the defined result.

The extracting unit may calculate the mixing ratio from the detectededge amounts from the plurality of edge detecting units, and maygenerate the edge extraction amount by mixing the edge amounts based onthe calculated mixing ratio.

Each of the plurality of spatial filters may have an adding unit foradding a pixel in concern and pixels which are located around the pixelin concern of the N-level image based on the weighing coefficient or thesize of the smoothing area.

The mixing unit may have a normalizing unit for normalizing the outputsaccording to a sum of the weighing coefficients and the gradation levelN.

The second object may be fulfilled by an area recognizing device forrecognizing an edge area and a non-edge area of an image which isexpressed by an N gradation level, the area recognizing devicecomprising a plurality of filters each of which detects an edgecomponent of a pixel in concern as referring to a value of a pixelwithin an area including the pixel in concern, the filters possessing anidentical edge detection characteristic and the pixel concern in each ofthe filters being set at a different location in the N-level gradationimage, an adding unit for comparing output from each of the plurality offilters with a certain value, and adding the output only when it isgreater than the certain value, a judging unit for comparing the addedresult from the adding unit with a certain judgement level, and outputsthe compared result as a judgement, and a recognizing unit forrecognizing a part of the image at which each of the filters is locatedas the edge area when the judgement outputted from the judging unit is afirst judgement, and recognizing it as the non-edge area when thejudgement is a second judgement level.

The area recognizing device may further comprise a setting unit forsetting a plurality of processing modes, or an image quality adjustmentvalue, and a first controlling unit for controlling the certainjudgement level according to a setting by the setting unit.

The second object may also be fulfilled by an area recognizing devicefor recognizing an edge area and a non-edge area of an image which isexpressed by an N gradation level, the area recognizing devicecomprising a plurality of first filters each of which detects an edgecomponent of a pixel in concern as referring to a value of a pixelwithin an area including the pixel in concern, the filters possessing anidentical edge detection characteristic and the pixel concern in each ofthe filters being set at a different location in the N-level gradationimage, a plurality of second filters which possess an identical edgedetection characteristic which is different from the plurality of firstfilters, and the pixel in concern in each of the filters is set at adifferent location in the N-level gradation image, a first adding unitfor adding the detected result outputted from each of the plurality offirst filters, a second adding unit for adding output from each of theplurality of second filters, a first judging unit for comparing theadded result outputted from the first adding unit with a first judgementlevel, and outputs the compared result as a judgement, a second judgingunit for comparing the added result from the second adding unit with asecond judgement level, and outputs the compared result as a judgement,and an area recognizing unit for recognizing whether a part of the imagewhere the filter is located is the edge area or the non-edge areaaccording to the judgements from the first judging unit and the secondjudging unit.

The first adding unit may compare each of the outputs from the pluralityof first filters with a first level, and obtain its output by adding theoutput which is greater than the first level, and the second adding unitmay compare each of the outputs from the plurality of second filterswith a second level, and obtain its output by adding the output which isgreater than the second level.

The area recognizing device may further comprise a setting unit forsetting a plurality of processing modes, or an image quality adjustmentvalue, and a second control unit for controlling validity of thejudgement from one of the first judging unit and the second judging unitaccording to a setting by the setting unit.

The third object may be fulfilled by a gradation level converting devicefor converting an image which is expressed by a K gradation level intoan image which is expressed by an N gradation level, then converting theN-level gradation image into an image which is expressed by an Mgradation level, N being smaller than K and M being larger than N, thegradation level converting device comprising a first converting unit forconverting the K-level gradation image data so that a value of eachpixel of the K-level gradation image data is centered around a median ofa signal range of the K-level gradation image data, an N-levelconverting unit for converting the K-level gradation image dataconverted by the first converting unit into the N-level gradation imagedata, an estimating unit for estimating the M-level gradation image datafrom the N-level gradation image data generated by the N-levelconverting unit, and a second converting unit which operates oppositelyto the first converting means to convert the M-level gradation imageestimated by the estimating unit.

The first converting unit may perform a non-linear conversion whichminimizes a fluctuation amount around the median of the signal range ofthe K-level gradation image data.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, advantages and features of the invention willbecome apparent from the following description thereof taken inconjunction with the accompanying drawings which illustrate a specificembodiment of the invention. In the drawings:

FIG. 1 is a block diagram depicting a gradation level converting devicewhich comprises an area recognizing device in a first embodiment of thepresent invention;

FIG. 2 is a block diagram depicting the gradation level convertingcircuit 1 in FIG. 1;

FIG. 3(a) shows a filter (1110) which is employed by a first addingcircuit 111, and FIG. 3(b) shows a filter (1210) which is employed by asecond adding circuit 121;

FIG. 4 is a block diagram depicting the first adding circuit 111;

FIG. 5(a) illustrates scanning of an image frame with the filter 1110,and FIG. 5(b) illustrates scanning of an image frame with the filter1210;

FIG. 6 is a block diagram depicting an improvement of an edge extractingcircuit 19;

FIG. 7(a) and FIG. 7(b) show a filter 1900 and a filter 1901 which areemployed by the edge extracting circuit 19, respectively;

FIG. 8 is a circuit block diagram depicting a filter 1500;

FIG. 9 illustrates spatial frequency characteristics of the filters inthe first embodiment;

FIG. 10(a) illustrates the filter 1500 which is employed by a first edgedetecting circuit 15, and FIGS. 10b and 10c illustrate filters 1600 and1601 which are employed by a second edge detecting circuit 16;

FIG. 11 shows the operation of a mixing circuit 13 in the firstembodiment;

FIG. 12 is a block diagram depicting a data converting circuit 7;

FIG. 13(a) shows a filter (1100) which explains setting of coefficientswith the data converting circuit 7, and FIG. 13(b) shows a dataconversion curve;

FIGS. 14a-14a show the operation of the data converting circuit 7;

FIG. 15 is a block diagram depicting an enhancing circuit 5;

FIG. 16 is a block diagram depicting a recognizing circuit 4;

FIG. 17(a) illustrates detection locations of an edge-detection filter41 and an edge-detection filter 42; FIG. 17(b) illustrates filters 4100and 4101 to be employed as the edge-detection filter 41; and FIGS. 17cand 17d illustrate filters 4200 and 4201 to be employed as theedge-detection filter 42;

FIG. 18 is a block diagram depicting an improvement of theedge-detection filter 41 and the edge-detection filter 42;

FIG. 19(a) is a pattern diagram showing binary data 100; FIG. 19(b)shows an edge output from a fifth detector A (41a) of the edge-detectionfilter 41; FIG. 19(c) shows an edge output ESA from the edge-detectionfilter 41; and FIG. 19(d) shows the edge output ESA from an improvededge-detection filter 41;

FIG. 20 is a block diagram depicting a gradation level converting devicein a second embodiment;

FIG. 21 is a block diagram depicting an N-level converting circuit 8;

FIG. 22(a) illustrates the operation of a first converting circuit 2 anda second converting circuit 3; FIG. 22(b) shows a relation between thelocation of a scanning aperture and the location of a pixel beforeconversion; and FIG. 22c shows a relation between the location of ascanning aperture and the location of a pixel after conversion;

FIG. 23(a) illustrates K-level image data; FIG. 23(b) illustrates binarydata which is obtained by converting the K-level image data in FIG.23(a); FIG. 23(c) illustrates output data of an estimation value NAwhich is obtained by estimating the binary data 100 in FIG. 23(b); FIG.23(d) illustrates binary data which is obtained by converting theK-level image data in FIG. 23(a) by a conversion curve 1, thenperforming binary conversion on it; FIG. 23(e) illustrates output dataof the estimation value NA which is obtained by estimating the binarydata 100 in FIG. 23(d); and FIG. 23(f) illustrates output data which isobtained by returning the estimation value NA into the original level bya conversion curve 2;

FIG. 24 illustrates a conversion table; and

FIG. 25 is a circuit block diagram depicting an n-th detector A of theedge-detection filter 41.

DESCRIPTION OF PREFERRED EMBODIMENTS

[Embodiment 1]

An area recognizing device and a gradation level converting devicerelating to a first embodiment of the present invention are described asreferring to the drawings.

FIG. 1 is a block diagram depicting the gradation level convertingdevice which comprises an area recognizing device A in the firstembodiment.

In FIG. 1, a gradation level converting circuit 1 converts N-levelgradation image data 100 into M-level gradation image data 200 (N<M).

An enhancing circuit 5 enhances the N-level gradation image data 100 oran estimation value NA (signal 300) from the gradation level convertingcircuit 1 to generate an enhancement signal EF (signal 500).

A data converting circuit 7 inputs the estimation value NA (signal 300)from the gradation level converting circuit 1, implements dataconversion pixel by pixel, and outputs a signal 700. This dataconversion enhances sharpness of the estimation value NA (signal 300).

A recognizing circuit 4 of the area recognizing device A outputs arecognition signal 400 (signal SE) for recognizing an edge area and anon-edge area of the N-level gradation image. The edge denotes a portionof the N-level gradation image where a large difference of densityexits, and the non-edge denotes a portion of the N-level gradation imagewhere neither a large nor any difference of density exists. For example,in the N-level gradation image being a portrait, the edge includes aborder between the background and a person and a border between hair andthe forehead of the person, while the non-edge includes a center part ofthe forehead.

A selecting circuit 6 selects inputs A-D by the recognition signal 400and a control signal 61 (CONT 1), and outputs a selection signal 600.Accordingly, either of a signal 200 (Xout), the signal 300 (estimationvalue NA), the signal 500 (EF), and the signal 700 is outputted as theselection signal 600.

The configuration of each of the circuits is described in detail.

FIG. 2 is a block diagram depicting the gradation level convertingcircuit 1. The gradation level converting circuit 1 for scanning theN-level gradation image pixel by pixel, and converting the N-levelgradation image into the M-level gradation image (N<M) comprises twomulti-level converting circuits 11 and 12, an edge extracting circuit19, a mixing circuit 13, and a quantizing circuit 14. Each of themulti-level converting circuits 11 and 12 comprises adding circuits 111and 121, and normalizing circuits 112 and 122 (hereunder, to distinguishbetween the circuit 11 and the circuit 12, the circuits 111 and 112composing the circuit 11 are referred to as first circuits while thecircuits 121 and 122 composing the circuit 12 are referred to as secondcircuits).

Each of the first and the second adding circuits 111 and 121 has afilter with a preset scanning aperture size. The first and the secondadding circuits 111 and 121 apply the filters to the inputted N-levelgradation image data in a range which is centered around a pixel inconcern, and adds each of the pixels obtained through the scanningaperture according to weighing coefficients set for each of the pixels.

FIG. 3(a) shows a filter 1110 of the first adding circuit 111, and FIG.3(b) shows a filter 1210 of the second adding circuit 121. A numeralwithin a window corresponding to each pixel of the filters 1110 and 1210denotes a weighing coefficient. As shown in FIG. 3(a) and 3(b), thescanning aperture size of the filter 1110 is 3×3 pixels, which issmaller than the scanning aperture size of the filter 1210 which is 5×5pixels. Accordingly, the first adding circuit 111 with the filter 1110provides a priority to resolution in the conversion processing, and thesecond adding circuit 121 with the filter 1210 provides a priority togradation in the conversion processing.

FIG. 4 shows the configuration of the first adding circuit 111. In FIG.4, a delaying circuit 113 outputs three lines of data including j-1, j,and j+1 from the inputted N-level gradation image data pixel by pixel.For example, the delaying circuit 113 comprises three line memories. Alatch circuit 117 delays a pixel signal by the time it takes to scan onepixel; numeral 116 denotes an adder; and a multiplier 114 multipliesinput data by an integer within a frame 115.

When a pixel P(i, j) is in concern, the output from the first addingcircuit 111 in FIG. 4 is shown in the following expression (1):

    SA=1·P(i-1, j-1)+2·P(i, j-1)+1·P(i+1, j-1)+

    2·P(i-1, j)+4·P(i, j)+2·P(i+1, j)+

    1·P(i-1, j+1)+2·P(i, j+1)+1·P(i+1, j+1)(1)

As shown in FIG. 5(a), the delaying circuit 113 generates one outputwhen the pixel in concern and the filter 1110 are shifted by one pixelin a scanning direction from the upper left hand corner of a frame ofthe N-level gradation image data. Accordingly, the output SA which isset forth above is obtained upon each shifting of the pixel in concernby one pixel. Although not illustrated, the configuration of the secondadding circuit 121 is substantially the same as the configuration of thefirst adding circuit 111 in FIG. 4 except that the number of the delayoutput lines, the number of the latch circuits, and numeral values bywhich the multiplier multiplies data are different. As shown in FIG.5(b), the second adding circuit 121 outputs an output SB upon eachshifting of the pixel in concern and the filter 1210 by one pixel in thescanning direction. The output SB from the second adding circuit 121 isshown in the following expression (2): ##EQU1##

With the thus constructed first adding circuit 111 and the second addingcircuit 121, when inputted image data is in the n-gradation level andthe total of weighing coefficients within the area which is subjected toaddition is Sn, a maximum number of output Mx is obtained by thefollowing expression (3):

    Mx=Sn·(n-1)                                       (3)

With the first adding circuit 111, when the total of weighingcoefficients is Sa and a maximum value of the output SA is Ma,Ma=(n-1)·Sa. If n=2, since the first adding circuit 111 performs theaddition based on the filter 1110 in FIG. 3(a), Sa=16 and Ma=16 areobtained from the weighing coefficients for the pixels.

Similarly, with the second adding circuit 121, when the total ofweighing coefficients is Sb and a maximum value of the output SB is Mb,Mb=(n-1)·Sb. If n=2, since the second adding circuit 121 performs theaddition based on the filter 1210 in FIG. 3(b), Sb=25 and Mb=25 areobtained from the weighing coefficients for the pixels.

Note that weighing coefficients provided to the filters for the firstadding circuit 111 and the second adding circuit 121 may be negativevalues and zeros besides positive values. Further, they may be realnumbers besides integers. When a coefficient "0" is assigned to a pixel,an input value is multiplied by "0", so that the multiplied result "0"is added. When the added result is negative, it is processed as "0" anda maximum value is obtained from the expression (3).

An area size (scanning aperture) where weighing coefficients are addedis a certain value or size which is determined beforehand in the processof designing. The scanning area size is determined according torequested image quality of the N-level gradation image data and theM-level gradation image data. Stated otherwise, the area size isdetermined according to characteristics of a spatial filter. Therefore,the same area size may be applied although different area sizes areprepared in the configuration set forth above. However, when the samearea size is employed, it is better to vary the degree of smoothness(smoothness of a spatial filter) by different weighing coefficients.

The first normalizing circuit 112 and the second normalizing circuit 122are described.

The first normalizing circuit 112 and the second normalizing circuit 122are well known circuits for converting the added results SA and SB fromthe adding circuits 111 and 121 into the M-level gradation image datawith the estimation values NA, NB based on the gradation level N and thetotal of weighing coefficients (N<M). More specifically, when an inputvalue is SGx and a maximum input value is Mx from the expression (3),the first normalizing circuit 112 and the second normalizing circuit 122normalize the input values into M-level gradation image data SCxaccording to the following expression (4). The [ ] represents omissionof decimals in converting into integers.

    SCx=[(SGx-Mx)·(M-1)+0.5]                          (4)

The expression (4) may be implemented by an operation circuit;otherwise, it may be implemented by a look-up table (hereunder, referredto an LUT) where SGx and SCsx are defined from the expression (4)beforehand and the calculated results are set in the table.

According to the expression (3) and (4), the input SGx which is theadded result is normalized into the M-level gradation image data. Morespecifically, the input SA is normalized into the estimation value NA bythe first normalizing circuit 112 which operates the followingexpression (5), and the input SB is normalized into the estimation valueNB by the second normalizing circuit 122 which operates the followingexpression (6). The [ ] represents omission of decimals in convertinginto integers.

    NA=[(SA-Ma)·(M-1)+0.5]                            (5)

    NB=[(SB-Mb)·(M-1)+0.5]                            (6)

If Ma=16, Mb=25, and M=256 in the first embodiment, the output NA forthe input SA and the output NB for the input SB are obtained, and eachof the outputs NA and NB is outputted to the mixing circuit 13 as256-level gradation image data.

It is apparent from the scanning aperture sizes of the filters 1110 and1120 of the first adding circuit 111 and the second adding circuit 121,the signal NA outputted from the first normalizing circuit 112 isM-level gradation image data which is superior in resolution althoughits gradation is not prominent, while the signal NB outputted from thesecond normalizing circuit 122 is M-level gradation image data which issuperior in gradation although its resolution is not prominent.

The mixing circuit 13 is explained by referring to FIGS. 2 and 11. FIG.11 shows the operation of the mixing circuit 13.

The weighing mixing coefficients Ka and Kb represent weighingcharacteristics a and c shown in FIG. 11, and they are determined basedon an signal Eout from the edge extracting circuit 19. The estimationvalue NA in FIG. 2 is multiplied by the coefficient Ka by the multiplier131, and the estimation value NB is multiplied by the coefficient Kb bythe multiplier 132. The multiplied results are added by the adder 133,and a mixing signal X is outputted.

When the estimation value NA (signal 300), the estimation value NB, andthe edge amount Eout (signal 181) are inputted, the mixing circuit 13generates the mixing signal X according to the following expression 7.MAX in the expression 7 represents a maximum value of the signal Eout(signal 181).

    X=Ka·NA+Kb·NB, in which Ka+Kb=1, Ka=Eout-MAX(7)

The mixing signal X obtained from the expression (7) is converted intoan integer by the quantizing circuit 14, then it is finally convertedinto the M-level gradation image data. For example, when M=256,256-level gradation image data Xout (signal 200) is generated.

Thus, the mixing circuit 13 puts weights to the plurality of inputsignals (the estimation values NA and NB) according to the edge amountEout (signal 181), and performs the mixing processing to generateinterpolated image data.

When the edge amount Eout is increased, the mixing circuit 13 increasesa mixing ratio of the estimation value NA to the estimation value NB sothat the former exceeds the latter. Accordingly, the degree ofsmoothness is decreased, and the same resolution is maintained.Oppositely, when the edge amount Eout is decreased, the mixing circuit13 increases a mixing ratio of the estimation value NB to the estimationvalue NA so that the former exceeds the latter. Accordingly, the degreeof smoothness is increased in the input image data. Consequently, thedegree of smoothness is enhanced in an area where the degree of edgechange is small, such as in a picture area.

As shown in FIG. 11, coefficients b and d which have binary changecharacteristics, MAX/2 may be utilized in the place of the coefficientsa and c which have linear change characteristics as the coefficients Kaand Kb. Either the coefficients b and d, or a and c are selected, andthis selection is desirable in processing a character. If the firstweighing control circuit 134 comprises a LUT stored in either a RAM or aROM, coefficients with indefinite change characteristics can be set aslong as Ka+Kb=1.

Thus, with interpolation processing of the mixing circuit 13, outputsfrom a plurality of different adding circuits (spatial filters) can bechanged continuously according to the edge amount Eout. As a result, thedegree of smoothness is changed locally depending on an edge componentof input image data, and the input image data can be converted intopreset multi-level data. By performing multi-level conversion processingwhile maintaining edges appropriately, both desirable resolution in acharacter-line area and desirable smoothness in a picture area can beachieved simultaneously. Especially in an area including both acharacter and a picture, deterioration of image due to recognitionerrors can be suppressed by a large amount since switching in binaryprocessing is eliminated.

Although two inputs are mixed with the mixing circuit 13, the sameeffects can be obtained even if more than two inputs are mixed. Also, byincreasing an area size which is subjected to addition (scanningaperture) or increasing the number of adding circuits with differentweighing coefficients, gradation image data with more continuity can begenerated.

Further, weights can be put to three or more inputs in parallel easily,so that the interpolation processing can be performed accurately.Although the linear weights are provided by the mixing circuit 13,non-linear weights are applicable.

The edge extracting circuit 19 is described by referring to FIGS. 7 and9. FIG. 7 illustrates filters to be employed by the edge extractingcircuit 19, and FIG. 9 shows spatial frequency characteristics of eachof the filters in the first embodiment.

The edge extracting circuit 19 employs the linear differential filters1900 and 1901 in FIG. 7. An edge component in a main scanning directioni and an edge component in a sub scanning direction j are detected withthe filters 1901 and 1900, respectively. Therefore, edge components ofthe N-level image in the main scan direction i and in the sub scanningdirection j can be extracted by combining an absolute value of the addedresult obtained by adding each of the coefficients of the filter 1900 todata and an absolute value of the added result obtained by adding eachof the coefficients of the filter 1901 to data. As shown in FIG. 9, thefilters 1900 and 1901 have spatial frequency characteristics whose peakis f0. With the thus featured filters, an edge component which iscentered around a specific frequency is extracted from the N-levelimage. Then, the extracted edge component is clipped to a maximum valueMAX, and is outputted to the mixing circuit 13 as the edge amount EOUT(signal 181).

Since different types of images are included herein, such as a pattern,a picture, and a character, it has been difficult to specify a spatialfrequency of edge components conventionally. Edges which are centeredaround a specific frequency can be extracted only when imagecharacteristics are defined or limited beforehand. In either of thecases, edge components in a desired edge area can be extracted.

However, usually a plurality of pages including different images areprocessed by a FAX, a document file, a word processor, and a copymachine; also a pattern, a picture, and a character may be included in asingle page. An edge extracting circuit which comprises a plurality ofedge detectors with different spatial frequencies, such as that in FIG.6, is needed to extract edge components of an edge area in each of thepattern area, the picture area, and the character area which areincluded in a single page.

An improvement of the edge extracting circuit 19 which can respond toedge components with different spatial frequencies is explained. FIG. 6is a block diagram depicting the improved edge extracting circuit 19.The edge extracting circuit 19 in FIG. 6 comprises a first edgedetecting circuit 15, a second edge detecting circuit 16, a mixingcircuit 17, and a clip circuit 18.

The first edge detecting circuit 15 and the second edge detectingcircuit 16 detect an edge component of the pixel in concern of theN-level gradation image.

The mixing circuit 17 mixes signal Ea and Eb representing the edgecomponents from the first edge detecting circuit 15 and the second edgedetecting circuit 16 with weighing coefficients Kc and Kd from a secondweighing control circuit 174, and outputs an edge amount Eg. The edgecomponent Ea from the first edge detecting circuit 15 is multiplied bythe coefficient Kc with a multiplying circuit 171, and the edgecomponent Eb from the second edge detecting circuit 16 is multiplied bythe coefficient Kd with a multiplying circuit 172. The multipliedresults are added by an adder 173, and the edge amount Eg is outputted.The mixing circuit 17 may be an LUT which is stored in a RAM or a ROMinstead of being the operation circuit.

The coefficients Kc and led are obtained from the following expression(8).

    Kc=Ea/(Ea+Eb+Cn),Kd=1-Kc                                   (8)

The edge amount Eg is obtained from the following expression (9).

    Eg=Kc-Ea+Kd·Eb                                    (9).

A constant Cn is smaller than the edge components Ea and Eb so thatrequired accuracy is obtained. For example, Cn=0.5.

The clip circuit 18 quantizes the edge amount Eg from the mixing circuit17, clips a maximum value into MAX, and outputs the clipped edge amountEout to the mixing circuit 13.

FIG. 10(a) shows a filter which is employed by the first edge detectingcircuit 15, and FIGS. 10b and 10c show a filter which is employed by thesecond edge detecting circuit 16.

The edge component Ea is generated by a quadratic differential filter1500 which is shown in FIG. 10(a), and an absolute value of the addedresult with the illustrated weighing coefficients is the edge amount Ea.The edge component Eb is generated by linear differential filters 1601and 1602 shown in FIGS. 10b and 10c, and the edge amount Eb is obtainedby adding absolute values of each of the added results to which theillustrated weighing coefficients are added.

FIG. 8 is a circuit block diagram depicting the quadratic differentialfilter 1500. A delaying circuit 150 delays the N-level image data 100 inthe sub scanning direction j by a line memory. The data which is delayedin the sub scanning direction j is further delayed in the main scanningdirection i by a latch. Subsequently, the delayed data is added by anadding circuit 152, an adding circuit 153, an adding circuit 159, and anadding circuit 160 according to coefficients for a filter to beemployed. A multiplier 154 multiplies output from the adding circuit 153by four, and a multiplier 157 multiplies output from the adding circuit159 by -1. An adder 155 outputs an edge component by adding outputs fromthe multiplier 157 and the multiplier 154. An absolute value calculatingcircuit 156 calculates an absolute value of the output from the adder155, and outputs it as the edge component Ea. FIG. 8 shows a circuitimplementing the quadratic differential filter. The circuit in FIG. 8operates in response to coefficients of the quadratic differentialfilter by increasing and decreasing the latches and the line memories,the latches delaying in the main scanning direction i and the linememories delaying in the sub scanning direction j.

Although not illustrated, the configuration of the second edge detectingcircuit 16 is basically the same as the first edge detecting circuit 15which comprises the delaying circuit including six line memories, thelatch circuit, the multiplier, the adder, the reversing device, and theabsolute value calculating circuit.

As shown in FIG. 9, spatial frequency characteristics of the edgecomponents Ea and Eb have different peak points fl and f2 in whichf1>f2. Consequently, the first edge detecting circuit 15 is suitable fordetection of a fine edge component (short cycle) while the second edgedetecting circuit 16 is suitable for detection of a coarse edgecomponent (long cycle).

With these different edge components Ea and Eb, the mixing circuit 17controls to increase a ratio for the bigger edge component according tothe expressions (8) and (9) set forth above. Accordingly, compared todetection based on a specific frequency f0, edge components can bedetected in a wider frequency range, so that edge components in edgeareas under different circumstances, such as in an edge of a picture, inan edge of a character, and in an edge of graphics can be detectedproperly. Although the quadratic differential filter for generating theedge component Ea and the linear differential filters for generating theedge component Eb are employed in FIG. 10(a) and FIGS. 10b and 10c,respectively, other detecting devices for detecting spatial frequenciescan be employed. Further, the same effects are obtained by mixingoutputs from more than three edge detecting devices.

Thus, by mixing edge components with different characteristics,switching in binary processing is eliminated, also edge components of animage can be extracted in a wide range of spatial frequencies. When theedge amount Eout is applied to the mixing circuit 13, the mixing can beperformed with an aperture perfectly according to regionalcharacteristics of edges. That is, even when images with differentcharacteristics, such as a character, graphics, and a picture areincluded, switching in binary processing is eliminated from theestimation processing completely, and regional characteristics of edgescan be responded to automatically. Thus, edges can be extracted fromeach area optimally, and the estimation processing is performedaccording to the edges. Therefore, negative effects of recognitionerrors due to regions are suppressed, and deterioration of image qualityis minimized. Further, the process of specifying an image is omitted.Accordingly, the time taken to specify an image is omitted, so that theentire processing can be completed at a high speed by the edgeextracting circuit 19 in FIG. 6. Also, indefinite images such as imagesreceived by a FAX can be processed optimally.

The quantizing circuit 14 quantizes the mixing signal X to convert itinto the M-level gradation image. Even if the estimation values NA andNB are converted into real numbers between 0 and 1, and they are mixedto generate a mixing signal which satisfies 0≦mixing signal X≦1, thequantizing circuit 14 re-quantizes the mixing signal into the requiredgradation level M by operating [X·(M-1)+0.5]. If the quantizing circuit14 is a memory where data can be replaced with another such as a RAM,the gradation level M which is required by a display device and aprinting device can be generated by re-quantization. Also, when theestimation values NA and NB are converted into integer values which arebetween 0 and (M-1), and they are mixed to generate a mixing signalwhich satisfies 0≦mixing signal X≦(M-1), the quantizing circuit 14operates [X+0.5], and re-quantizes the mixing signal into the requiredgradation level M as setting a maximum value M-1.

The data converting circuit 7 is described by referring to FIGS.12-14a-14c. The data converting circuit 7 enhances sharpness of theestimation value NA outputted from the first multi-level convertingcircuit 11 of the gradation level converting circuit 1.

FIG. 12 is a block diagram depicting the data converting circuit 7; FIG.13(a) shows a filter for setting a coefficient to the data convertingcircuit 7; FIG. 13(b) shows data conversion curves; and FIG. 14 showsthe operation of the data converting circuit 7. It is assumed that N=2and M=17 and a normalizing coefficient for the first normalizing circuit112 is 1(SA=NA).

In FIG. 12, the estimation value NA (signal 300) as an input isgenerated by the first multi-level converting circuit 11 in FIG. 2. Thefirst adding circuit 111 of the first multi-level converting circuit 11employs the filter 1110 shown in FIG. 3(a).

The coefficient setting circuit 71 of the data converting circuit 7 setsa data conversion coefficient to a coefficient register 72. The dataconversion coefficient is calculated according to weighing coefficientsof the filter 1110 which is employed by the first adding circuit 111 ofthe first multi-level converting circuit 11. The process of calculationis described later.

A value "4" stored in a register 72a is subtracted from the estimationvalue NA (signal 300) outputted from the first multi-level convertingcircuit 11 with a subtractor 77, and the subtracted result is multipliedby a value "2" stored in a register 72c with a multiplier 78. As aresult, a signal 780 is obtained. A comparing circuit 73 converts anoutput signal 730 into "H" level when A>B for inputs, and converts itinto "L" level when A<B. A selecting circuit 75 selects a value "16"from a register 72d when the output signal 730 from the comparingcircuit 73 is in "H" level, and selects the output signal 780 when theoutput signal 730 from the comparing circuit 73 is in "L" level, andoutputs an output signal 750. A comparing circuit 74 converts an outputsignal 740 into "L" level when A<B for inputs, and converts it into "H"level when A≧B. An eliminating circuit 76 converts an output signal into"0" when the signal 740 is in "L" level, and outputs the output signal750 as the output signal 700 when the signal 740 is in "H" level.

The process of calculating a value in each of the registers 72a-72d tobe set to the coefficient setting register 72 is described.

FIG. 13a shows the filter 1110 as an embodiment. When weighingcoefficients W11-W33 correspond to each of the pixel locations, "1" isset to W11, W13, W31, and W33; "2" is set to W12, W21, W23, and W32; and"4" is set to W22. Although weighing coefficients are not specifiedusually, better effects are obtained when a coefficient for the pixel inconcern is larger than those for other pixels. If a coefficient isdefined in the main scanning direction and the sub scanning direction,better image quality is obtained since it is not dependent on adirection any longer.

When register values reg(a), reg(b), reg(c), and reg(c) are stored inregisters 72a, 72b, 72c, and 72d, each of the register values iscalculated by the following expression 10.

    reg(a)=W11+W12+W13

    reg(d)=W11+W12+W13+W21+W22+W23+W31+W32+W33

    reg(b)=reg(d)-reg(a)

    reg(c)=reg(d)-(reg(b)-reg(a))                              (10)

Although weighing coefficients located at the top row are added incalculating the reg(a), weighing coefficients located at one of the toprow, the bottom row, the right end column and left end column may beadded instead. The total of weighing coefficients is detected incalculating the reg(d).

In the expression (10), an estimation value which is obtained supposedlyfor pixels located at one end row or one end column is eliminated fromthe estimation value NA beforehand so that a scanning aperture isreduced equivalently in data conversion. Accordingly, the degree ofsharpness is improved. Even when the scanning aperture size is enlarged,an estimation value for pixels located at one end row or one end columnis eliminated from the estimation value, so that the same effects areobtained.

According to this data conversion, the inputted estimation value NA(signal 300) is converted by a data conversion curve in FIG. 13(b), andthe converted result is outputted as the output signal 700.

FIGS. 14a-14c compare the estimation value NA (300) obtained when binaryimage data is shifted in the main scanning direction i by the filter1110 in FIG. 13(a) with the signal 700 obtained by processing theestimation value NA by the data converting circuit 7. Consequently, thedegree of sharpness (resolution) is enhanced.

By applying this conversion signal 700 to an input D of the selectingcircuit 6, the degree of sharpness (resolution) in an edge arearecognized by the recognizing circuit 4 can be enhanced. Also, since anyrecognition error in a non-edge area does not cause a radical densitychange, deterioration of image quality can be suppressed by large.

If each of the values (72a-72d) to be set to the register 72 of the dataconverting circuit 7 is specified, the data converting circuit 7 may bereplaced with a ROM. In this case, a data conversion table whichcorresponds to data conversion coefficients is stored internally, andthe same data conversion is performed. On the other hand, if each of thevalues (72a-72d) to be set to the register 72 of the data convertingcircuit 7 is not specified, the data converting circuit 7 may bereplaced with a RAM. In this case, a coefficient corresponding to aweighing coefficient of the filter 1110 is calculated; a conversiontable corresponding to a conversion curve is regenerated; and there-generated conversion table is set to the RAM again.

The enhancing circuit 5 is described by referring to FIG. 15.

In FIG. 15, the delaying circuit 50 comprises three line memories, anddelays the estimation value NA (signal 300) in the sub scanningdirection j. The delayed data is further delayed in the main scanningdirection i by the delaying circuit 51. The delaying circuit 51comprises a latch. A multiplying circuit 52 multiplies a value P (i, j)for a pixel to be enhanced by five. An adding circuit 54 adds pixelvalues P(i-1, j-1), P(i+1, j-1), P (i-1, j+1), and P(i+1, j+1). Amultiplying circuit 56 multiplies output from the adding circuit 54 by-1. An adding circuit 53 adds outputs from the multiplying circuits 52and 56. An absolute value calculating circuit 55 calculates an absolutevalue of output from the adding circuit 53, and outputs the enhancementsignal EF (signal 500).

The enhancing circuit 5 enhances the pixel in concern of the image withthe estimation value NA (signal 300). Accordingly, the image is enhancedpixel by pixel. When the pixel P(i, j) is enhanced, the enhancementsignal EF is obtained from the following expression (11), in which anabsolute value is calculated from ABS[ ].

    EF=ABS[5·P(i,j)-P(i, j-1)-P(i+1, j-1)-P(i-1, j+1)-P(i+1, j+1)](11)

Note that the same enhancement signal EF (signal 500) can be generatedwithout delaying the estimation value NA (signal 300) by the three linememory. In this case, five locations including the pixel in concern (i,j), that is (i, j), (i-1, j-1), (i+1, j-1), (i-1, j+1), and (i+1, j+1)are supposed as estimation locations; the estimation value NA of each ofthese five locations is calculated by the first multi-level convertingcircuit 11 in FIG. 2; and the calculated estimation values for the fivelocations, NA(i, j), NA(i-1, j-1), NA(i+1, j-1), NA(i-1, j+1), andNA(i+1, j+1) are substituted to the expression 11 as pixel values forthe locations. Consequently, the enhancement signal EF (signal 500) canbe obtained directly from the N-level gradation image 100.

By obtaining the enhancement signal EF directly from the N-levelgradation image 100, memory capacity of the three line memory of thedelaying circuit 50 is reduced by a large amount. Since N=2, M=256 inthe first embodiment, eight bits per one pixel are delayed by the signal300 while one bit per one pixel is delayed in the N-level gradationimage 100. If the number of delay lines corresponds to this ratio,memory capacity of the line memory is reduced into 1/8. When theenhancement signal EF is obtained directly from the N-level gradationimage 100, the size of the first multi-level converting circuit 11 isenlarged by five times since five pixels are concerned simultaneously.However, by comparing the number of elements corresponding to thereduced memory capacity 7/8 with the number of elements corresponding to5 times larger than the size of the first multi-level converting circuit11, it is found that a smaller number of elements are obtained bygaining the enhancement signal EF (signal 500) directly from the N-levelgradation image 100. This is advantageous for an LSI inplementationsince it can be performed at a higher speed.

With the enhancing circuit 5 as set forth, an enhancement image with theestimation value NA is obtained where sharpness is enhanced. By applyingthe enhancement signal 500 (signal EF) to an edge area of the N-levelgradation image, deterioration of edge sharpness can be suppressed.Also, by emphasizing only an edge area, the M-level gradation image canbe obtained without deteriorating smoothness in a non-edge area. As aresult, the M-level gradation image (N<M) where edges are maintained inan edge area and high degree of smoothness is maintained in a non-edgearea can be obtained. Note that not the enhancing circuit 5 but theselecting circuit 6, which will be described later, applies theenhancement signal to an edge area of the N-level gradation image.

The operation of the recognizing circuit 4 of the area recognizingdevice A is described by referring to FIGS. 16-19.

FIG. 16 is a block diagram depicting the recognizing circuit 4; FIG.17(a) shows detection locations of an edge-detection filter 41 and anedge-detection filter 42; FIGS. 17b and 17d illustrate theedge-detection filter 41; and FIG. 17(c) illustrates the edge-detectionfilter 42. Although N and M are not specified, it is assumed that N=2and M=256 in the following description.

In FIG. 16, the edge-detection filter 41 comprises nine detectors whichare a first detector A(41a) through a 9-th detector A(41i), and an adder410 for adding outputs from these nine detectors (41a-41i) to generatean additional signal ESA. As shown in FIG. 17(a), when the location of apixel in concern to be processed by the gradation level convertingcircuit 1 is E, these nine detectors (41a-41i) detect an edge amountbased on detection locations A-I which includes the location E. The samefilters are used for the detectors 41a-41i. For example, edge componentsare extracted by the linear deferential filters 4100 and 4101 shown inFIG. 17(b), and an edge amount is calculated by adding an absolute valueof each of the added results with weighing coefficients of the filters.

FIG. 25 is a circuit block diagram implementing an n-th detector A(41a-41i) of the edge-detection filter 41. Herein, n is between 1 and 9.A delaying circuit 418 delays the N-level image data 100 in the subscanning direction j by a line memory. The data delayed in the subscanning direction j is further delayed in the main scanning direction iby a latch. Depending on a coefficient of a filter to be employed,addition is performed on the data by adding circuits 412a-412d. Theadder 413 calculates a difference relating to the sub scanning directionj, and the absolute value calculating circuit 415 calculates an absolutevalue of the output from the adder 413. Accordingly, an edge componentin the sub scanning direction (edge component relating to the filter4101) is outputted. The adder 417 outputs an edge component of the pixelin concern by adding the edge component in the main scanning direction iand the edge component in the sub scanning direction j. FIG. 25 shows acircuit implementing a linear differential filter. The circuit in FIG.25 responds to coefficients of a filter to be employed by increasing anddecreasing the latches and the line memories, the latches delaying inthe main scanning direction i and the line memories delaying in the subscanning direction.

Similarly, the edge-detection filter 42 comprises nine detectors whichare a first detector B(42a) through a 9-th detector B(42i), and an adder420 for adding outputs from these nine detectors (42a-42i) to generatean addition signal ESB. As shown in FIG. 17(a), when the location of thepixel in concern to be processed by the gradation level convertingcircuit 1 is E, these nine detectors detect an edge amount based ondetection locations A-I which include the location E. The same filtersare used for the detectors 42a-42i. For example, edge components areextracted by the linear deferential filters 4200 and 4201 shown in FIG.17(c), and an edge amount is calculated by adding an absolute value ofeach of the added results with the weighing coefficients of the filters.

As shown in FIG. 9, spatial frequency characteristics of the filters4100 and 4101 have a peak frequency which is different from the filters4200 and 4201.

When a peak frequency for the filters 4100 and 4101 is FSa and a peakfrequency for the filters 4200 and 4201 is FSb, FSa>FSb. Therefore, theedge-detection filter 41 is suitable for detection of an edge with highfrequencies, such as a edge in a character and a figure, and theedge-detection filter 42 is suitable for detection of an edge with lowfrequencies, such as an edge in a picture which is not influenced by atexture or a mesh-dot frequency which is determined from the mesh pitch.

The recognizing circuit 4 detects an edge amount by adding the detectedresults from a plurality of detectors, so that its output is increasedwhen the detectors are closely related to each other. Accordingly, edgescan be detected without any influence of a texture, such as a patternwhich is unique to binary processing.

In FIG. 16, the first comparing circuit 43 compares the signal ESA witha comparison level CPA (signal 64), and outputs [H] when ESA>CPA whileit outputs [L] when ESA≦CPA. Similarly, the second comparing circuit 44compares the signal ESB with a comparison level CPB (signal 65), andoutputs [H] when ESB>CPB while it outputs [L] when ESB≦CPB. [H] and [L]are judgement levels by which an edge area and a non-edge area aredetected respectively.

An AND circuit 45 validates output from the first comparing circuit 43when the control signal 62 (signal CONT 2) is in [H] level. On the otherhand, the AND circuit 45 invalidates output from the first comparingcircuit 43 when the control signal (signal CONT 2) is in [L] level, andmaintains [L] level for every output. Similarly, an AND circuit 46validates output from the second comparing circuit 44 when the controlsignal 63 (signal CONT 3) is in [H] level. On the other hand, the ANDcircuit 46 invalidates output from the second comparing circuit 44 whenthe control signal 63 is in [L] level, and maintains [L] level for everyoutput.

An OR circuit 47 outputs the recognition signal 400 in [H] level wheneither of the outputs from the AND circuit 45 or the AND circuit 46 isin [H] level, and outputs the recognition signal 400 in [L] level whenboth of the outputs from the AND circuit 45 and the AND circuit 46 arein [L] level. [H] and [L] levels are judgment levels by which an edgearea and a non-edge area are detected respectively.

Thus, the first comparing circuit 43 and the second comparing circuit 44utilize the comparison levels CPA and CPB as parameters, and judgesvalidity of the detection signals ESA and ESB which are obtained byadding edge signals from a plurality of detection locations.

As shown in FIG. 9, a recognition area "a" is detected by the comparisonlevel CPA, and a recognition area "b" is detected by the comparisonlevel CPB. Accordingly, frequency areas which are recognized by theparameters CPA and CPB can be controlled.

The control signal 62 controls the validating and invalidating of thejudged result from the first comparing circuit 43, while the controlsignal 63 controls the validating and invalidating of the judged resultfrom the second comparing circuit 44. Accordingly, a edge area can bedetected in images with different optimal conditions, such as in apicture image, in a character image, in graphics, in a mesh-point image,and in a mixed image of character and picture appropriately, anddetection characteristics can be controlled.

FIG. 18 shows an improvement of the recognizing circuit 4 of FIG. 16.With the recognizing circuit of FIG. 18, each of the outputs from thenine detectors (41a-41j) is compared with a value in the register 411,and the outputs which are higher than the value in the register 411 areadded by the adder 410. Similarly, each of the outputs from the ninedetectors (42a-42j) is compared with a value in a register 421, and theoutputs which are higher than the value in the register 42 are added bythe adder 420.

The effects of these improvements are explained by referring to FIG. 19(a), 19(b), 19(c), and 19(d).

FIG. 19(a) is a pattern diagram of the binary data 100. An area 2000represents a character•graphic area, and an area 2200 of the area 2000shows an example where data is dispersed by binary processing, and imagedata of character•graphic is shifted. This data shifting does not occurwith electronic data of computer graphics, but frequently occurs with anoriginal document scanned with a scanner or the like whose documentdensity is small or blurred. An area 2100 in a non-edge area representsan isolation point which is frequently generated by binary processingwhen K-level image data has a small density value. That is, if theK-level image data is converted into binary data according to digitalhalftone technique, such as an error-diffusion method or a dithermethod, a constant value in a certain area of the K-level image data isrepresented by the density of dots.

FIG. 19(b) illustrates outputs when edges of an image are detected by afifth detector A (41e) of the edge-detection filter 41. The fifthdetector A (41e) detects edges of an image in the main scanningdirection and the sub scanning direction with the filter 4100 and 4101,and detects edges of the pixel in concern by combining the outputs fromthe filters 4100 and 4101. An area is recognized based on output datarepresenting the detected edges. According to digital halftonetechniques, such as an error-diffusion method or a dither method, evenwhen data is constant, it is dispersed by binary processing.Accordingly, the edge output Eg will never be "0". A judgement level isset "3" or more to reduce the effects of the isolation point area 2000in the non-edge area (recognition error). In this case, areas 3000enclosed with a dot line in FIG. 19(b) are detected as edge areas.Although no isolation point area 2000 is detected by mistake in FIG.19(b), the character•graphic area 2000 is not detected, but areaslocated outside the area 2000 are detected. Further, the detected areas3000 are located in isolation due to the area 2200 in FIG. 19(a) whosedata is shifted by binary processing. The size of an area to be detectedcan be increased by decreasing the judgement level to "2"; however, theisolation of the area 3000 is not solved. Also, detection errors will beincreased around the isolation point area 2100.

FIG. 19(c) illustrates the edge output ESA from the edge-detectionfilter 41. If the comparison level CPA of the first comparing circuit 43in FIG. 16 is set "8", an area 3000 enclosed with a dot line in FIG.19(c) is detected as an edge area. Although the problem of an isolationpoint area 2100 still remains, a broad area including a picture•graphicarea 2000 can be detected without a break. Also, the detected area 3000is not influenced by the area 2000 in FIG. 19(a) where data is shiftedby binary processing. Only the picture•graphic area 2000 will bedetected if "15" is set as the comparison level CPA. Thus, differentareas are extracted by setting different values as the comparison levelCPA; accordingly, different recognition areas are obtained.

FIG. 19(d) illustrates the edge output ESA from the improvededge-detection filter 41. A comparison value (REG1) is set "1", and edgeoutputs Eg from the nine edge detectors (41a-41i) are compared to thecomparison value by comparing circuits (41j-41r) respectively.Subsequently, the adder 410 adds the edge output Eg only when Eg>1. If"8" is set as the comparison level CPA of the first comparing circuit43, an area 3000 enclosed with a dot line in FIG. 19(d) is detected asan edge area. The detection error of the isolation point area 2100 isavoided, also a broad area including the character•graphic area 2000 isdetected without a break. Further, the detected area 3000 is notinfluenced by the area 2200 in FIG. 19(a) where data is shifted. Forthese reasons, detection accuracy is enhanced in FIG. 19(d) than in FIG.19(c). Similarly, detection accuracy of the edge-detection filter 42will be enhanced. Also, by setting "12" as the comparison level CPA,only the character•graphic area 2000 can be extracted. Thus, differentarea can be extracted by setting different values as the comparisonlevel CPA. Accordingly, different recognition areas are obtained.

Thus, a character•graphic being an edge area is detected accurately byadding edge amounts which are detected by a plurality of edge-detectionfilters from a plurality of detection areas including the pixel inconcern, then comparing the added result with a certain level. Further,dispersion of data generated during binary processing will not influencethe detection any longer if edge amounts detected by edge-detectionfilters are compared with a certain value, and only the edge amountswhich are greater than the certain value are added. By doing so, errordetection due to a texture of a binary image can be eliminated. As aresult, edge detection accuracy is improved by reducing error detectionsin a non-edge area, while adding edge amounts in an edge area.

The recognizing circuit 4 in FIGS. 16 and 18 divides the N-levelgradation image into an edge area and a non-edge area, so that differentgradation conversion processing can be performed on each of the areas.Both desired resolution and uniformity can be achieved simultaneously ingradation conversion processing of the N-level gradation image byapplying the most suitable gradation conversion processing to each ofthe areas.

Also, around an edge area, an n×m scanning aperture outputs theestimation value NA which represents an intermediate image from binarydata including the character•graphic area 2000 being an edge area and anon-edge area. With respect to image quality of the intermediate image,high resolution is preferred. Therefore, the edge area must berecognized. To detect the area including the character•graphic area 2000as an edge area, a broad area including the picture•graphic area 2000needs to be detected without a break by the n×m scanning aperture. Forthis reason, the recognizing circuit 4 in FIGS. 16 and 18 which detectsa broad area including the character•graphic area 2000 without a breakis suitably employed in gradation level conversion.

The selecting circuit 6 is described as referring to FIG. 1.

The selecting circuit 6 selects input from the input A (signal 200), theinput B (signal 300), the input C (signal. 200), and the input D (signal500 or signal 700) by the control signal 61 (CONT1) and the recognitionsignal 400 (signal SE), then outputs the selection signal 600.

For example, Table 1 shows selection conditions. It is assumed that theselection signal 600 is OUT. Also, the signal 400 (signal SE) is in [H]level when an edge area is detected, and it is in [L] level when anon-edge area is detected

                  TABLE 1                                                         ______________________________________                                        CONT1     S E    OUT         PROCESSING MODE                                  ______________________________________                                        0         L      A           picture                                                    H      B                                                            1         L      C           character picture                                          H      D                                                            2         L      B           character                                                  H      D                                                            ______________________________________                                    

In Table 1, the control signal 61 (signal CONT1) has three signallevels. The selecting circuit 6 selects input according to a level ofthe control signal 61. When the signal level is "0", the input A isselected for a non-edge area, and the input B is selected for an edgearea. When the signal level is "1", the input C is selected for anon-edge area, and the input D is selected for an edge area. When thesignal level is "2", the input B is selected for a non-edge area, andthe input D is selected for an edge area.

Since gradation characteristics are taken seriously in "picture mode",the inputs A and B are selected, and enhancement of an edge area is notperformed. Since resolution and gradation are taken seriously in"character•picture mode", the inputs C and D are selected, andenhancement of an edge area is performed. Since resolution is takenseriously in "character mode", the inputs B and D are selected, andenhancement of an edge area is performed.

Processing modes in Table 1 are set with a control panel 91, and thecontrolling circuit 92 sets the respective control signal 61 (CONT1).Accordingly, conversion from the N-level gradation image into theM-level gradation image (N<M) can be performed with images in differentprocessing modes.

Also, the output signal 500 from the enhancing circuit 5 can be replacedwith the output signal 700 from the data converting circuit 7. The dataconverting circuit 7 eliminates an estimation value which is obtainedfor pixels located at either one end row or one end column from theestimation value NA beforehand. Therefore, a scanning aperture isreduced equivalently during data conversion. As a result, the degree ofsharpness is enhanced.

Thus, according to the gradation level converting device with theconfiguration set forth above, edge deterioration is reduced in an edgearea, and desired smoothness can be obtained in a non-edge area of theobtained M-level gradation image (N<M).

[Embodiment 2]

A gradation level converting device in a second embodiment of thepresent invention is described as referring to the drawings.

FIG. 20 is a block diagram depicting the gradation level convertingdevice in the second embodiment.

Although the N-level gradation image data is converted into the M-levelgradation image data in the first embodiment, K-level gradation imagedata (K>N) is converted into the N-level gradation image data, then theconverted N-level gradation image data is converted into the M-levelgradation image data in this embodiment. For example, this device issuitably employed in an image storing system where the high level (Klevel) gradation image data is converted into the low level (N level)gradation image data to be stored into a disc, then the low levelgradation image data is returned into the high-level (M level) gradationimage in reading.

In FIG. 20, a first converting circuit 2 comprises an LUT stored in anRAM, and it converts the K-level image data according to a conversiontable which is set by a controlling circuit 92 via a control signal 920(signal TAB1). The first converting circuit 2 will be described indetail later in the description of operation.

An N-level converting circuit 8 converts the K-level image data from thefirst converting circuit 2 into the N-level gradation image data (K>N).

An image memory 9 is a semi-conductor memory, a hard disk device, or thelike, and it stores the N-level gradation image data received from theN-level converting circuit 8.

An estimating circuit 93 is utilized in the place of the gradation levelconverting device in FIG. 1 relating to the first embodiment, and itconverts the N-level gradation image data from the image memory 9 intothe M-level gradation image data 600 (N<M).

A second converting circuit 3 is a LUT such as a RAM, and it convertsthe M-level gradation image data according to a conversion table set bya controlling circuit 92 via a control signal 921 (signal TAB 2). Thesecond converting circuit 3 is described in detail later in thedescription of operation.

The controlling circuit 92 outputs control signals to the estimatingcircuit 93, the first converting circuit 2, and the second convertingcircuit 3 according to setting signals from a control panel 91.

For example, the control panel 91 comprises a plurality of keys, andoutputs setting signals according to the settings of an input switch atthe control circuit 92 which include the setting of resolution level ofan image, and the mode settings between "picture image mode","character•picture image mode", "character image mode","character•graphic image mode" and "mesh-dot image mode".

The operation of the thus constructed gradation level converting deviceis described.

The configuration of the N-level converting circuit 8 for converting theK-level image into the N-level image (K>N) is described as referring toFIG. 21. A dither method or an error diffusion method may be employed toconvert the K-level image into the N-level image. In this embodiment,the K-level image data is converted into the N-level image dataaccording to the error dispersion method shown in FIG. 21.

In FIG. 21, K-level image data fxy is added to an integration errorvalue Err located around a pixel in concern by an adder 81, and theadder 81 outputs f'xy. The output f'xy is compared to a plurality ofthreshold values which are set by the N-level converting circuit 82beforehand so that the output f'xy is quantized into an N value, and theN-level converting circuit 82 outputs gxy. The adder 83 operatesexy=f'xy-gxy; a quantization error is distributed to around the pixel inconcern with some weighing coefficient, and it is stored into an errormemory 86. A multiplier 84 multiplies an error e(x+i, y+j) which isdistributed around the pixel in concern by a weighing coefficient of aweight matrix Wij, and outputs the integration error value Err. A signal801 is expressed by Err=ΣΣe(x+i, y+j). Wij. For example, when N=2, gxyoutputs binary data of "0" and "255", and a normalizing circuit 87normalizes the binary data of "0" and "255" into binary data of "0" and"1". Accordingly, the K-level image data is converted into the N-levelimage data.

When a halftone image which is generated according to theerror-diffusion method in FIG. 21 is employed as an input image to anestimating circuit 93, estimation can be restored with desiredresolution and gradation without any deterioration of edges or any falsegray-scale contour. Although the error diffusion method is employedherein, other methods of storing average data value of an image can beemployed. More specifically, a well-known N-level converting means (K>N)can be employed. For example, an average error minimizing method, adither method, and a multi-value dither method may be employed instead.

The first converting circuit 2 and the second converting circuit 3 aredescribed as referring to FIGS. 22-24. FIG. 22 (a) shows the conversionoperation of the first converting circuit 2 and the second convertingcircuit 3; FIG. 22(b) shows relation between the location of a scanningaperture and the location of black pixels; FIG. 23 illustrates dataconversion; and FIG. 24 illustrates a conversion table.

N=2, M=256, and K=256 are set in the second embodiment although they canbe other values. Also, for convenience of the description, theestimation value NA is applied to the first multi-level convertingcircuit 11 of the gradation level converting circuit 1 in FIG. 2.Estimation operation is described as assuming that the filter 1110 witha 2×2 scanning aperture is applied to the first adding circuit 111, andevery weighing coefficient is 1. The first normalizing circuit 112substitutes Ma=4, M=256 to the expression 5, and outputs the estimationvalue NA. (M-1)/Ma is around 64, so that 64 data value is estimated forone black pixel within the 2×2 aperture area.

A conversion curve 3 in FIG. 22(a) is set to the first convertingcircuit 2 and the second converting circuit 3. Yout=Xin for input signalXin and output signal Yout is satisfied with the conversion curve 3.Stated otherwise, it is a conversion table for utilizing an input signalas an output signal.

The estimating circuit 93 estimates the M-level gradation image from theN-level gradation image (N<M). The K-level image which is inputted tothe N-level converting circuit 8 will be estimated if K=M. The N-levelconverting circuit 8 converts density data of the pixel in concern intobinary data of "0" and "255", and disperses a generated error to pixelssurrounding the concerned pixel. Binary data of "0" and "255" isnormalized into binary data of "0" and "1", and it is stored in theimage memory 9. According to this binary quantization processing, 8bits/pixel is converted into 1 bit/pixel; accordingly, data capacity iscompressed into 1/8. The compressed image has a bit map structure, and acompressing rate is a fixed length. Compared to compression at avariable length compressing rate, compression at a fixed lengthcompressing rate is more suitable for conversion in real time since acompression time is constant. Also, image data in the image memory 9 hasa bit map structure, and the process of expanding an image which wascompressed at a variable length compressing rate can be eliminated.Consequently, edition can be performed at a high speed. If this binarydata is outputted to an output device which expresses "0" by a white dotand "1" by a black dot, or to a display device which expresses "1" by awhite dot and "0" by a black dot, a digital halftone image based oncompression of dots can be obtained.

The first multi-level converting circuit 11 estimates the M-levelgradation image from binary data of "0" and "1" stored in the imagememory 9. For example, the K-level image data with a certain value "16"is converted into normalization data "0" and "1" by the N-levelconverting circuit 8. If "0" and "1" are expressed by a white pixel anda black pixel, respectively, binary data of "0" and "1" in FIG. 22(b)which is Dx distance in the main scanning direction and is Dy distancein the sub scanning direction is obtained. The first multi-levelconverting circuit 11 estimates the M-level gradation image by addingdata values within an aperture, and performing the normalization. Whenthe scanning aperture is 2×2, and every weighing coefficient to be addedis 1, M=256 and Ma=4; accordingly, a black dot "1" is converted intodata value 64 by the expression 4.

When the K-level data is small, that is being within a highlight area ofthe image, the scanning aperture size is smaller than a diffusion rangeof black pixels ("1" of binary data), and the estimation value NA variesdepending on where the scan. For a scan position A, a scan position B,and a scan position C in FIG. 22(b), the estimation value is 64, 0, and64, respectively. Thus, with respect to the K-level image data of acertain value "16", the estimation value is not constant. Similarly, ina shadow area, the aperture size is smaller than a dispersion range ofwhite pixels ("0" of binary data), and the estimation value NA isfluctuated.

To reduce the fluctuation of the estimation value NA in a highlight areaand a shadow area, a conversion curve 1 and a conversion curve 2 in FIG.22(a) are set to the first converting circuit 2 and the secondconverting circuit 3, respectively.

A principle of the conversion is described as referring to FIGS. 22(a),(b), and (c). Conversion of the K-level image data at a point Hl in FIG.22 (a) is described. Also, "0" and "1" of binary data are illustrated bya white and a black pixel, respectively.

As shown in FIG. 22(a), input data (point H) whose value is 16 ismultiplied by four to be output data (point B). This quadrupled K-levelimage data is converted into "0" and "1" by the N-level convertingcircuit 8. Theoretically, the input data is quadrupled, so that thedensity of black pixels is quadrupled, and it is converted into binarydata of "0" and "1" which is Dx/2 distance in the main scanningdirection and is Dy/2 distance in the sub scanning direction in FIG.22(c). The estimation value NA is 64, 64, and 64 for the positions A, B,and C in FIG. 22(c). Since the K-level image data is multiplied by fourby the conversion curve 1, they are divided by four by a conversioncurve 2 (point E); accordingly, the estimation value becomes 16, 16, and16 for the positions A, B, and C respectively. Consequently, fluctuationin the estimation value NA is reduced.

A point G in FIG. 22(a) is a median of a signal range for the K-levelimage data, and it is located at 128 when an input signal is within thesignal range between 0 and 255. The first converting circuit 2 convertsthe K-level image data to be greater than a line AG in a highlight area(for example, a curve linking points A, B, and G), and converts theK-level image data to be smaller than a line GD in a shadow area (forexample, a curve linking points G, C, and D). Accordingly, a diffusionrange of black pixels ("1" of binary data) in a highlight area and adiffusion range of white pixels ("0" of binary data) in a shadow areaare reduced.

The second converting circuit 3 returns the converted result from thefirst converting circuit 2 into the original value. The secondconverting circuit 3 returns the converted value represented by thecurve ABG in a highlight area which is smaller than the point G to acurve linking points A, E, and G, and returns the converted valuerepresented by the curve GCD in a shadow area which is greater than thepoint G to a curve linking points G, F, and D.

Thus, by converting the K-level image data to center around a median ofthe signal range with the first converting circuit, and returning theestimation value NA for the M-level image data into the original valueby the second converting circuit 3, fluctuation in the estimation valueNA in either a highlight area or a shadow area is reduced.

The principle of the conversion processing is set forth above, and it isdetailed by referring to FIGS. 23(a)-23(f).

FIG. 23(a) shows the K-level image data in an embodiment.

A maximum value and a minimum value of the K-level image data are 255and 0 in the following description. A certain value "16" which is around1/16 of the maximum value 256 is processed.

FIG. 23(b) shows an example of the binary data 100 which is obtained byperforming binary conversion on the K-level image data in FIG. 23(a) bythe N-level converting circuit 8. As shown in FIG. 23(a), a single "1"exists in an area A(4×4) of the binary image data.

FIG. 23(c) shows the estimation value NA which is estimated by the firstmulti-level converting circuit 11 from the binary data 100 in FIG.23(b). Although an average value of the area A is "16" and the K-levelimage data is estimated from this average value, the estimation value ineach of the pixel positions fluctuates largely, that is between 0 and64.

FIG. 23(d) shows the binary data 100 which is obtained by multiplyingthe K-level image data "16" in FIG. 23(a) by four with a conversioncurve 1 (conversion 1), and performing binary processing on "64" whichis around 1/4 of the maximum value 255 by the N-level converting circuit8. Four "1"s exist in the area A of the binary data in FIG. 23(d).

FIG. 23(e) shows the estimation value NA which is estimated from thebinary data 100 in FIG. 23(d) by the first multi-level convertingcircuit 11.

FIG. 23(f) shows an output which is obtained by reducing the estimationvalue NA in FIG. 23(e) into 1/4 by a conversion curve 2 (conversion 2).Thus, the estimation value NA in FIG. 23(e) is reduced by 1/4 since theN-level converting circuit 8 multiplied the K-level image data input byfour with the conversion curve 1 (conversion 1). The estimation value NAin each of the pixel locations does not fluctuate in the area A in FIG.23(e), also the K-level image data "16" is estimated accurately.

The effects of the conversion is described. Compared to the estimationvalue NA without any conversion in FIG. 23(c), fluctuation in theestimation value NA is surely suppressed in FIG. 23(f) because of theconversion by the first converting circuit 2 and the second convertingcircuit 3. Further, the accurate estimation value is obtained.Especially, these effects are significant in a highlight area and ashadow area where binary data are likely to diffuse.

Thus, the K-level image data is converted to center around a median of asignal range by the first converting circuit 2, then the convertedK-level image data is converted into the N-level data (K>N). Therefore,diffusion range of black pixels and white pixels can be suppressed in ahighlight area and a shadow area, respectively. Also, by returning theestimation value NA for the M-level data (N<M) which is obtained fromthe N-level data, fluctuation in the estimation value is avoided in ahighlight area and a shadow area. Further, the estimation value can beobtained accurately. Accordingly, a diffusion range of data which occursduring binary processing by the N-level converting circuit 8 iscontrolled, and diffusion ranges of white pixels "0" and black pixels"1" are suppressed to be within a short distance from the pixel inconcern. Consequently, the estimation value NA with little regionalfluctuation can be obtained.

The first converting circuit 2 and the second converting circuit 3suppress diffusion ranges of white pixels "0" and black pixels "1" in ahighlight area and a shadow area, respectively so that data is diffusedwithin a short distance from the pixel in concern. Accordingly, theestimation value NA can be obtained as suppressing regional fluctuation.

Also, by suppressing diffusion ranges of white pixels "0" and blackpixels "1" from the pixel in concern in a highlight area and a shadowarea respectively with the first converting circuit 2 and the secondconverting circuit 3, fluctuation in the estimation value can besuppressed even if a scanning aperture is reduced. By reducing the sizeof a scanning aperture, the size of a circuit is reduced, so thatresolution is enhanced.

When the M-level image data with few fluctuation in the estimation valueis outputted to an output device or to a display device, a high qualitydigital halftone image with little ununiformity in density can beobtained.

Although the gradation level converting device in FIG. 1 is employed asthe estimating circuit 93, the gradation level converting circuit 1, thefirst multi-level converting circuit 11, or the multi-level convertingcircuit 12 in FIG. 2 may take the place of the estimating circuit 93.That is, the present invention can be applied to every device forconverting the N-level gradation image into the M-level gradation image(N<M) by estimating a multi level from a pixel value within a certainscanning aperture.

Also, fluctuation amount in the estimation value is determined by aconversion amount by the first converting circuit 2 and an aperture sizeof the filter 1110 of the first adding circuit 111. If the aperture sizeis constant, greater effects are obtained by a larger conversion amount;however, a change in a line BC in FIG. 22(a) is reduced, and a detectionamount for an edge component is suppressed. This is more significantwith a device for controlling the aperture size according to a detectededge component. For example, a greater change in the line BC isdesirable in a character image and a graphic image.

Accordingly, to be responsive to different images, control keys by which"processing mode" is selected are constructed on the control panel 91,and the setting signals which correspond to the control keys areoutputted to the controlling circuit 92. The controlling circuit 92 hasa conversion memory internally, and stores a plurality of conversiontables. In response to "processing mode" which is set with the controlpanel 91, a conversion table is set to the first converting circuit 2and the second converting circuit 3 via the signals 920 and 921.

As shown in FIG. 24, for example, a conversion table for adjustment ofimage quality is stored in the conversion memory. When "picture•graphicmode" which takes edges seriously is set, a table for the conversioncurve 3 is read from a head address of an LUT 3. Subsequently, theconversion table is set to the first converting circuit 2 via the signal920, and to the second converting circuit 3 via the signal 921.

Similarly, if "picture mode" which takes smoothness seriously is set, atable for the conversion curve 1 is read from a head address of the LUT1, and it is set to the first converting circuit 2 via the signal 920.Simultaneously, a table for the conversion curve 2 is read from a headaddress of the LUT 2, and it is set to the second converting circuit 3via the signal 921.

As shown in FIG. 24, different images can be processed and image qualityis adjusted by storing a plurality of conversion tables in theconversion memory.

Further, the estimating circuit 93 can process different images andadjust image quality by controlling the control signals 61-65 from thecontrolling circuit 92. The operation of the estimating circuit 93 isdescribed as referring to FIG. 16, Tables 1, and 2.

                  TABLE 2                                                         ______________________________________                                        CONT1 CONT2    CONT3   CPA   CPB  PROCESSING MODE                             ______________________________________                                        0     L        L       --    --   picture                                     1     L        H       --    B1   character.picture                           2     H        H       A2    B2   character                                   ______________________________________                                    

As shown in Table 2, the control signal 61 (signal CONT1) selects anestimation output according to "processing mode", such as the presenceor absence of enhancement (or the presence or absence of dataconversion). The control signal 62 (signal CONT 2) and the controlsignal 63 (signal CONT 3) control the recognition signal 400 in FIG. 16.The control signal 63 (signal CONT 3) controls the AND circuit 45 inFIG. 16, and controls validity (invalidity) of the compared result fromthe first comparing circuit 43. The control signal 63 (signal CONT 3)also controls the AND circuit 63 in FIG. 16, and controls validity(invalidity) of the compared result from the second comparing circuit44. "H" and "L" levels represent validity and invalidity, respectively.The comparison levels CPA and CPB control recognition levels. When thecomparison levels (CPA, CPB) are increased, greater edge detectionlevels are required, so that a recognition area is limited. In contrast,when the comparison levels (CPA, CPB) are decreased, recognition can beperformed according to smaller edge detection levels, so that arecognition area is broadened.

A control value which is the most suitable for each "processing mode" isset with these control signals.

Smoothness is taken seriously in "picture mode", so that recognition isnot performed. Accordingly, "L" is set both to the CONT2 and CONT3. Inthis setting, the comparison levels CPA and CPB are invalid.

Since both resolution in a character area and smoothness in a picturearea are taken seriously in "character•graphic mode", recognition isperformed. However, considering a recognition error due to a mesh-dotpoint image whose spatial frequency is spread in a wide range, therecognition signal which employs the edge-detection filter 41 with highrecognition frequency is judged to be invalid, and a recognition signalwhich employs the edge detection filter 42 with recognition frequencywhich is smaller than mesh-dot frequency and with few recognition errordue to a mesh-dot frequency is judged to be valid. Accordingly, "L" and"H" are set to the CONT 2 and CONT 3, respectively. In this setting, avalue of the comparison level CPA is invalid, and a value of thecomparison level CPB is valid.

Recognition is performed in "character mode" since resolution of acharacter area is taken seriously. Further, to obtain the recognitionsignal for detecting spatial frequency in a wide range, the detectedresults from the edge-detection filter 41 and the edge-detection filter42 are validated. Accordingly, "H" is set both to the CONT 2 and theCONT 3. In this setting, values of the comparison levels CPA and CPB arevalid. Further, to enlarge an edge detection area, a value of thecomparison level CPB is shifted from B1 to B2 (B1>B2).

A plurality of image quality setting keys are constructed at the controlpanel 91. With these setting keys, the controlling circuit 92 adjustsimage quality by setting the comparison levels CPA and CPB to aplurality of setting steps, and controlling a recognition area.

Thus, in the second embodiment, the estimating circuit 93 can processvarious types of images and adjusts image quality by controlling thecontrol signals 61 through 65. Therefore, in converting the N-levelgradation image into the M-level gradation image (N<M), the conversionprocessing is performed in responsive to different images, and imagequality is adjusted to be desirable in the conversion processing.Consequently, the high quality M-level gradation image (N<M) can begenerated.

The edge area detecting means in the first embodiment and the secondembodiment is not influenced by fluctuation in dispersion of data inconverting the K-level image into the N-level image (K>N). Therefore, anedge area and a non-edge area of the N-level gradation image can berecognized accurately.

Further, a means for processing differently an edge area and a non-edgearea of the N-level image is included, so that the optimal M-level image(N<M) can be estimated both in an edge area and a non-edge area of theN-level image (N<M).

Also, a converting means for suppressing a diffusion range of data in ahighlight area or a shadow area in converting the K-level image into theN-level image (I(>N) is included, and the N-level image data isestimated from the converted N-level image data. As a result,fluctuation in the estimation value can be suppressed.

A means for performing conversion which is the most suitable for each ofdifferent images in converting the N-level gradation image into theM-level gradation image (N<M), and a means for adjusting image qualityas desired are included. With these means, the high quality M-levelgradation image (N<M) can be generated.

By applying the present invention to a copy machine, a printer, adisplay device, a FAX, and a document file, recording and transfer ofdata can be performed with a reduction in the required image datacapacity, and a high quality estimation image can be obtained at theoutput and display stages. The present invention can be applied not onlyto black-and-white images, but also to colored images at a outputdevice, a display device, and a FAX. Similarly, the required image datacapacity can be reduced while still maintaining high image quality. Thesame operation is applied to each of the colors in an RGB image or a YMCimage.

The area recognizing device and the gradation level converting device inthe first and the second embodiments of the present invention can beimplemented by operation processing (software processing) which employsa CPU or a DSP.

If image data is compressed or expanded by the gradation levelconverting device in the present invention, coding at a fixed lengthcoding rate is achieved, and a bit map structure is maintained in thecoded image data. The coding is completed within a fixed time if data iscoded at a fixed length rate; accordingly, this coding is suitable for adevice which processes in real time. Further, by maintaining a bit mapstructure, over-writing can be performed at any location of an imagerecording device. Since the process of expanding a compressed image canbe omitted, the edition can be performed at a high speed.

Although the present invention has been fully described by way ofexamples with reference to the accompanying drawings, it is to be notedthat various changes and modifications will be apparent to those skilledin the art. Therefore, unless otherwise such changes and modificationsdepart from the scope of the present invention, they should be construedas being included therein.

What is claimed is:
 1. An area recognizing device for recognizing anedge area and a non-edge area of an image which is expressed by an Ngradation level, said area recognizing device comprising:at least twoedge detecting filters, each of which detects an edge component of apixel in concern as referring to a plurality of pixels within an areaincluding said pixel in concern and outputs an edge component value as adetecting result, wherein each of said edge detecting filters is placedto target a different pixel in said N-level gradation image as saidpixel in concern; at least two comparing means, each of whichcorresponds to one of said edge detecting filters to have a one-to-onerelation, compares said edge component value outputted from acorresponding edge detecting filter with a certain value, and judgessaid edge component value to be valid when said edge component value isequal to or greater than said certain value; an adding means for summingup each edge component value judged to be valid; and a judging means forjudging a summed up result obtained from said adding means at a certainjudgment level, and outputting a certain judgment result, wherein saidedge area and said non-edge area of said N-level gradation image isrecognized by said certain judgment result.
 2. The area recognizingdevice defined in claim 1, wherein each of said edge detecting filtershas an identical edge detecting characteristic.
 3. The area recognizingdevice defined in claim 1, wherein said certain value is changeable toadjust a level at which said edge component is judged to be valid. 4.The area recognizing device defined in claim 1, wherein said certainjudgment level, which represents a boundary of said edge area and saidnon-edge area, is adjustable.
 5. The area recognizing device defined inclaim 1 further comprises a setting means for setting either of aplurality of processing modes and a plurality of image qualityadjustment values, wherein said certain judgment level is controlledaccording to either of said plurality of processing modes and saidplurality of image quality adjustment values set by said setting means.6. The area recognizing device defined in claim 5, wherein N of saidN-level gradation image is
 2. 7. An area recognizing device forrecognizing an edge area and a non-edge area of an image which isexpressed by an N gradation level, said area recognizing devicecomprising:at least two edge detecting filters, each of which detects anedge component of a pixel in concern as referring to a plurality ofpixels within an area including said pixel in concern and outputs anedge component value as a detecting result, wherein each of said edgedetecting filters has a different spatial frequency characteristic fordetecting said edge component; and at least two judging means, each ofwhich corresponds to one of said edge detecting filters to have aone-to-one relation and judges said edge component value outputted froma corresponding edge detecting filter at a certain judgment level andoutputs a certain judgment result, wherein said edge area and saidnon-edge area of said N-level gradation image are recognized by saidcertain judgment result obtained from each of said judging means.
 8. Thearea recognizing device defined in claim 7, wherein each certainjudgment result, obtained from each of said judging means andcorresponding to one of said edge detecting filters, is processedseparately to be one of valid and invalid.
 9. The area recognizingdevice defined in claim 8 further comprises a setting means for settingeither of a plurality of processing modes and a plurality of imagequality adjustment values, wherein each certain judgment result whichcorresponds to one of said edge detecting filters is processed to be oneof valid and invalid according to either of said plurality of processingmodes and said plurality of image quality adjustment values set by saidsetting means.
 10. The area recognizing device defined in claim 9,wherein N of said N-level gradation image is 2.